Hierarchical Land Cover and Vegetation Classification using Multispectral Data Acquired from an Unmanned Aerial Vehicle

Oumer Ahmed
Adam Shemrock
Dominique Chabot
Chris Dillon
Griffin Williams
Rachel Wasson
Steven Franklin
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The use of multispectral cameras deployed on unmanned aerial vehicles (UAVs) in land cover and vegetation mapping applications continues to improve and receive increasing recognition and adoption by resource management and forest survey practitioners. Comparisons of different camera data and platform performance characteristics are an important contribution in understanding the role and operational capability of this technology. In this article, object-based classification accuracies for different cover types and vegetation species of interest in central Ontario were examined using data from three UAV-based multispectral cameras. Five land-cover classes (forest, shrub, herbaceous, bare soil, and built-up) were determined to be up to 95% correct overall with calibrated multispectral Parrot Sequoia digital camera data compared to independent field observations. The levels of classification accuracy decreased approximately 10–15% when spectrally less capable consumer-grade RGB sensors were used. Multispectral Parrot Sequoia classification accuracy was approximately 89% when more detailed vegetation classes, including individual deciduous tree species, shrub communities and agricultural crops, were analysed. Additional work is suggested in the use of such UAV multispectral and point cloud data in ash tree discrimination to support emerald ash borer infestation detection and management, and in analysis of functional and structural vegetation characteristics (e.g. leaf area index).